Image-based Estimation of Pelvic Skeletal Key Points of Dairy Cattle by Using Deep Learning

Abstract

Dystocia among dairy cows results in economic losses. Such losses can be prevented, and the survival of newborn calves can be ensured, by predicting the degree of calving difficulty in cows. Although pelvic cavity measurements have been commonly used to predict calving difficulty, these are difficult to obtain because measurement requires considerable time and effort. Hence, a simpler and more practical alternative to pelvic cavity measurements must be developed. Here, we applied the deep learning algorithm of the convolutional pose machine (CPM), a model for estimating joint positions, to predict the degree of calving difficulty in Holstein dairy cows by estimating key points on the lower limbs and pelvis from images. Side and back view images were obtained. The image data were augmented for training and validation to determine the skeletal key points. The data were augmented to Training (n=189,125) and Validation (n=48,790) sets to learn the skeletal key points. A total of 23 key points related to the pelvic shape of the cattle were identified, and the percentage of correct key points was 0.89. Therefore, skeletal key points in cattle can be estimated by using CPM, and the pelvic skeleton recognition system developed here can be used to construct a system for estimating the degree of calving difficulty on the basis of images.